以蟻群演算法應用於國軍主動運補路徑規劃之研究－以某後勤區九類零附件為例

國軍後勤部隊因人、車精簡，在主動運補任務時，常以一條路線對後勤區內所屬單位遂行支援，但當受補單位增加，除無法一一現勘外亦無法以作業研究法規劃最佳路徑；則需藉由TSP發展出啟發式演算法求解。本研究藉現行之Google Earth導航功能，以某後勤區聯保廠為例，使用蟻群演算法建構一條函蓋所支援二級場最短距離的主運路徑；再以分枝界線法第一、二回合所求下限為基線，與傳統習慣（貪婪法）比較，發現參數在β=2效果不佳外，其餘皆可提昇10%~30%，最高達31.78%。可於主運任務前以此計算模式有效規劃路徑，縮短距離，提昇效率。另再得一管理之意涵，組織需一些思維不同的人，雖會造成困擾，但可能也是創造績效、解決難題的主力。提供管理者參考及後續研究。

關鍵字

主動運補；路徑組合；蟻群演算法； TSP之求解；最佳路徑

並列摘要

Due to the shortages of Military logistics units of stuffs and vehicles during Active transportation and supply's mission, it often needs a route for all units in logistical area to support the team. But when the backed up units increased, it won't be able to inspect all areas and can't use the Operations Research to map up the best path. The way is to use TSP to solve the problem by developing a Heuristic Algorithm. This study is through the path finding function of Google Earth and takes the logistic area of Combined Maintenance Facility as an example. It uses the Ant Colony Algorithm to construct an O-level and use the Branch and Bound which covers the needs of lower bound of the first and the second rounds. And we compare it with the traditional way, Greedy method. We found out that it is not good for the parameter of β=2. But the rest of them can be increased by 10%~30%. The highest point can reach to 31.78%. By using this calculation, we can plan the routs efficiently, shorten the distance and raise the efficiency. By the way, regarding with the management interface, the organization needs some people who have unique ideas. They may cause some problems, but they may be able to be the main power to create achievement and solve problems. The above information provides for managers’ reference and following up discussions.

並列關鍵字

Active transportation and supply ； Route combinations ； Ant colony algorithm ； solving the TSP ； Best path

國際替代計量

以蟻群演算法應用於國軍主動運補路徑規劃之研究－以某後勤區九類零附件為例

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